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Investigating the efficiency of deep learning methods in estimating GPS geodetic velocity / Omid Memarian Sorkhabi in Earth and space science, vol 9 n° 10 (October 2022)
[article]
Titre : Investigating the efficiency of deep learning methods in estimating GPS geodetic velocity Type de document : Article/Communication Auteurs : Omid Memarian Sorkhabi, Auteur ; Muhammed Milani, Auteur ; Seyed Mehdi Seyed Alizadeh, Auteur Année de publication : 2022 Article en page(s) : 8 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] apprentissage profond
[Termes IGN] champ de vitesse
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] géodynamique
[Termes IGN] point géodésique
[Termes IGN] positionnement par GPS
[Termes IGN] station GPS
[Termes IGN] tectoniqueRésumé : (auteur) Geodetic velocity (GV) has many applications in tectonic motion determination and geodynamic studies. Due to the high cost of global navigation satellite system stations, deep learning methods have been investigated to estimate GV. In this research, four methods of convolutional neural networks (CNNs), deep Boltzmann machines, deep belief net and recurrent neural networks have been applied. The GV of 42 global positioning system stations is entered the deep learning methods. The outputs of the four methods have successfully passed the normality test. The results show that the CNN method has a lower goodness of fit and root mean square error (RMSE). CNN can learn different dependencies and extract features. Numéro de notice : A2022-757 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1029/2021EA002202 Date de publication en ligne : 22/09/2022 En ligne : https://doi.org/10.1029/2021EA002202 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101763
in Earth and space science > vol 9 n° 10 (October 2022) . - 8 p.[article]A relation-augmented embedded graph attention network for remote sensing object detection / Shu Tian in IEEE Transactions on geoscience and remote sensing, vol 60 n° 10 (October 2022)
[article]
Titre : A relation-augmented embedded graph attention network for remote sensing object detection Type de document : Article/Communication Auteurs : Shu Tian, Auteur ; Lihong Kang, Auteur ; Xiangwei Xing, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1000718 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] graphe
[Termes IGN] image à haute résolution
[Termes IGN] information sémantique
[Termes IGN] relation sémantique
[Termes IGN] relation spatiale
[Termes IGN] réseau neuronal de graphes
[Termes IGN] SIFT (algorithme)Résumé : (auteur) Multiclass geospatial object detection in high spatial resolution remote sensing imagery (HSRI) is still a challenging task. The main reason is that the objects in HRSI are location-variable and semantic-confusable, which results in the difficulties in differentiating the complicated spatial patterns and deriving the implicitly semantic labels among different categories of objects. In this article, we propose a relation-augmented embedded graph attention network (EGAT), which enables the full exploitation of the underlying spatial and semantic relations among objects for improving the detection performance. Specifically, we first construct two sets of spatial and semantic graphs of objects–objects for object relations modeling. Second, a Siamese architecture-based embedding spatial and semantic graph attention network is designed for relations reasoning, which is implemented by introducing the long short-term memory (LSTM) mechanism into the EGAT, for learning the relations among different categories of intraobjects and interobjects. Driven by the spatial and semantic LSTM, the EGAT-LSTM can adaptively focus on the critical information of reason graphs for spatial–semantic correlation discrimination in the embedding non-Euclidean feature space. By this way, the EGAT-LSTM can effectively capture the global and local spatial–semantic relationships of objects–objects, and then produce relations-augmented features for improving the performance of object detection. We conduct comprehensive experiments on three public datasets for multiclass geospatial object detection. Our method achieves state-of-the-art performance, which demonstrates the superiority and effectiveness of the proposed method. Numéro de notice : A2022-766 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3073269 Date de publication en ligne : 18/05/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3073269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101788
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 10 (October 2022) . - n° 1000718[article]Semi-supervised adversarial recognition of refined window structures for inverse procedural façade modelling / Han Hu in ISPRS Journal of photogrammetry and remote sensing, vol 192 (October 2022)
[article]
Titre : Semi-supervised adversarial recognition of refined window structures for inverse procedural façade modelling Type de document : Article/Communication Auteurs : Han Hu, Auteur ; Xinrong Liang, Auteur ; Yulin Ding, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 215 - 231 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification semi-dirigée
[Termes IGN] échantillonnage de données
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] façade
[Termes IGN] fenêtre (bâtiment)
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] photographie aérienne oblique
[Termes IGN] réseau antagoniste génératifRésumé : (auteur) Deep learning methods are typically data-hungry and require many labelled samples. Unfortunately, the amount of effort required to label the data has significantly hindered the application of deep learning methods, especially in 3D modelling tasks requiring heterogeneous samples. This paper proposes a semi-supervised adversarial recognition strategy embedded in the inverse procedural modelling engine to reduce data annotation costs for learning to model 3D façades. Beginning with textured level-of-details models, we use convolutional neural networks to recognise the types and estimate the parameters of windows from image patches. The window types and parameters are then assembled into the procedural grammar. A simple procedural engine is built inside off-the-shelf 3D modelling software, producing fine-grained window geometries. To obtain a useful model from a few labelled samples, we leverage a generative adversarial network to train the feature extractor in a semi-supervised manner. The adversarial training strategy exploits the unlabelled data to stabilise the training phase. Experiments using publicly available façade image datasets reveal that the proposed methods can improve classification accuracy and parameter estimation by approximately 10% and 50%, respectively, under the same network structure. In addition, performance gains are more pronounced when testing against unseen data featuring different façade styles. Numéro de notice : A2022-666 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.08.014 Date de publication en ligne : 30/08/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.08.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101528
in ISPRS Journal of photogrammetry and remote sensing > vol 192 (October 2022) . - pp 215 - 231[article]Single-image super-resolution for remote sensing images using a deep generative adversarial network with local and global attention mechanisms / Yadong Li in IEEE Transactions on geoscience and remote sensing, vol 60 n° 10 (October 2022)
[article]
Titre : Single-image super-resolution for remote sensing images using a deep generative adversarial network with local and global attention mechanisms Type de document : Article/Communication Auteurs : Yadong Li, Auteur ; Sébastien Mavromatis, Auteur ; Feng Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 3000224 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image isolée
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] pouvoir de résolution spectrale
[Termes IGN] reconstruction d'image
[Termes IGN] réseau antagoniste génératifRésumé : (auteur) Super-resolution (SR) technology is an important way to improve spatial resolution under the condition of sensor hardware limitations. With the development of deep learning (DL), some DL-based SR models have achieved state-of-the-art performance, especially the convolutional neural network (CNN). However, considering that remote sensing images usually contain a variety of ground scenes and objects with different scales, orientations, and spectral characteristics, previous works usually treat important and unnecessary features equally or only apply different weights in the local receptive field, which ignores long-range dependencies; it is still a challenging task to exploit features on different levels and reconstruct images with realistic details. To address these problems, an attention-based generative adversarial network (SRAGAN) is proposed in this article, which applies both local and global attention mechanisms. Specifically, we apply local attention in the SR model to focus on structural components of the earth’s surface that require more attention, and global attention is used to capture long-range interdependencies in the channel and spatial dimensions to further refine details. To optimize the adversarial learning process, we also use local and global attentions in the discriminator model to enhance the discriminative ability and apply the gradient penalty in the form of hinge loss and loss function that combines L1 pixel loss, L1 perceptual loss, and relativistic adversarial loss to promote rich details. The experiments show that SRAGAN can achieve performance improvements and reconstruct better details compared with current state-of-the-art SR methods. A series of ablation investigations and model analyses validate the efficiency and effectiveness of our method. Numéro de notice : A2022-767 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3093043 Date de publication en ligne : 12/07/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3093043 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101789
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 10 (October 2022) . - n° 3000224[article]Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions / Di Zhu in Geoinformatica, vol 26 n° 4 (October 2022)
[article]
Titre : Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions Type de document : Article/Communication Auteurs : Di Zhu, Auteur ; Yu Liu, Auteur ; Xin Yao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 645 - 676 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse multivariée
[Termes IGN] analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] distribution spatiale
[Termes IGN] échantillonnage
[Termes IGN] intelligence artificielle
[Termes IGN] régression
[Termes IGN] régression géographiquement pondérée
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau neuronal de graphesMots-clés libres : Geospatial artificial intelligence (GeoAI) Résumé : (auteur) Geospatial artificial intelligence (GeoAI) has emerged as a subfield of GIScience that uses artificial intelligence approaches and machine learning techniques for geographic knowledge discovery. The non-regularity of data structures has recently led to different variants of graph neural networks in the field of computer science, with graph convolutional neural networks being one of the most prominent that operate on non-euclidean structured data where the numbers of nodes connections vary and the nodes are unordered. These networks use graph convolution – commonly known as filters or kernels – in place of general matrix multiplication in at least one of their layers. This paper suggests spatial regression graph convolutional neural networks (SRGCNNs) as a deep learning paradigm that is capable of handling a wide range of geographical tasks where multivariate spatial data needs modeling and prediction. The feasibility of SRGCNNs lies in the feature propagation mechanisms, the spatial locality nature, and a semi-supervised training strategy. In the experiments, this paper demonstrates the operation of SRGCNNs with social media check-in data in Beijing and house price data in San Diego. The results indicate that a well-trained SRGCNN model is capable of learning from samples and performing reasonable predictions for unobserved locations. The paper also presents the effectiveness of incorporating the idea of geographically weighted regression for handling heterogeneity between locations in the model approach. Compared to conventional spatial regression approaches, SRGCNN-based models tend to generate much more accurate and stable results, especially when the sampling ratio is low. This study offers to bridge the methodological gap between graph deep learning and spatial regression analytics. The proposed idea serves as an example to illustrate how spatial analytics can be combined with state-of-the-art deep learning models, and to enlighten future research at the front of GeoAI. Numéro de notice : A2022-865 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1007/s10707-021-00454-x Date de publication en ligne : 02/11/2021 En ligne : https://doi.org/10.1007/s10707-021-00454-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102158
in Geoinformatica > vol 26 n° 4 (October 2022) . - pp 645 - 676[article]Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding / Faxi Yuan in Computers, Environment and Urban Systems, vol 97 (October 2022)PermalinkThe fractional vegetation cover (FVC) and associated driving factors of modeling in mining areas / Jun Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 10 (October 2022)PermalinkComparison of deep neural networks in detecting field grapevine diseases using transfer learning / Antonios Morellos in Remote sensing, vol 14 n° 18 (September-2 2022)PermalinkAn improved multi-task pointwise network for segmentation of building roofs in airborne laser scanning point clouds / Chaoquan Zhang in Photogrammetric record, vol 37 n° 179 (September 2022)PermalinkCrowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images / Ekrem Saralioglu in Geocarto international, vol 37 n° 18 ([01/09/2022])PermalinkDeep image deblurring: A survey / Kaihao Zhang in International journal of computer vision, vol 130 n° 9 (September 2022)PermalinkDeep learning–based monitoring sustainable decision support system for energy building to smart cities with remote sensing techniques / Wang Yue in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 9 (September 2022)PermalinkDeep learning method for Chinese multisource point of interest matching / Pengpeng Li in Computers, Environment and Urban Systems, vol 96 (September 2022)PermalinkFlood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach / Quoc Bao Pham in Natural Hazards, vol 113 n° 2 (September 2022)PermalinkMapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression / Haoyu Wang in Remote sensing of environment, vol 278 (September 2022)PermalinkSimulation of land use/land cover changes and urban expansion in Estonia by a hybrid ANN-CA-MCA model and utilizing spectral-textural indices / Najmeh Mozaffaree Pour in Environmental Monitoring and Assessment, vol 194 n° 9 (September 2022)PermalinkStructured binary neural networks for image recognition / Bohan Zhuang in International journal of computer vision, vol 130 n° 9 (September 2022)Permalink3D building reconstruction from single street view images using deep learning / Hui En Pang in International journal of applied Earth observation and geoinformation, vol 112 (August 2022)PermalinkAn automatic approach for tree species detection and profile estimation of urban street trees using deep learning and Google street view images / Kwanghun Choi in ISPRS Journal of photogrammetry and remote sensing, vol 190 (August 2022)PermalinkDeep learning feature representation for image matching under large viewpoint and viewing direction change / Lin Chen in ISPRS Journal of photogrammetry and remote sensing, vol 190 (August 2022)PermalinkLocation-aware neural graph collaborative filtering / Shengwen Li in International journal of geographical information science IJGIS, vol 36 n° 8 (August 2022)PermalinkA pipeline for automated processing of Corona KH-4 (1962-1972) stereo imagery / Sajid Ghuffar in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)PermalinkPredicting vegetation stratum occupancy from airborne LiDAR data with deep learning / Ekaterina Kalinicheva in International journal of applied Earth observation and geoinformation, vol 112 (August 2022)PermalinkSmart city data science: Towards data-driven smart cities with open research issues / Iqbal H. Sarker in Internet of Things, vol 19 (August 2022)PermalinkSpatial–spectral attention network guided with change magnitude image for land cover change detection using remote sensing images / Zhiyong Lv in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)PermalinkTransfer learning from citizen science photographs enables plant species identification in UAV imagery / Salim Soltani in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 5 (August 2022)PermalinkUsing attributes explicitly reflecting user preference in a self-attention network for next POI recommendation / Ruijing Li in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)PermalinkA model development on GIS-driven data to predict temporal daily collision through integrating Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms; case study: Tehran-Qazvin freeway / Reza Sanayeia in Geocarto international, vol 37 n° 14 ([20/07/2022])PermalinkDetection of diseased pine trees in unmanned aerial vehicle images by using deep convolutional neural networks / Gensheng Hu in Geocarto international, vol 37 n° 12 ([01/07/2022])PermalinkExploring the vertical dimension of street view image based on deep learning: a case study on lowest floor elevation estimation / Huan Ning in International journal of geographical information science IJGIS, vol 36 n° 7 (juillet 2022)PermalinkA framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method / Yongyang Xu in Computers, Environment and Urban Systems, vol 95 (July 2022)PermalinkGlobal forecasting of ionospheric vertical total electron contents via ConvLSTM with spectrum analysis / Jinpei Chen in GPS solutions, vol 26 n° 3 (July 2022)PermalinkImproving remote sensing classification: A deep-learning-assisted model / Tsimur Davydzenka in Computers & geosciences, vol 164 (July 2022)PermalinkModeling human–human interaction with attention-based high-order GCN for trajectory prediction / Yanyan Fang in The Visual Computer, vol 38 n° 7 (July 2022)PermalinkPolyline simplification based on the artificial neural network with constraints of generalization knowledge / Jiawei Du in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)PermalinkA second-order attention network for glacial lake segmentation from remotely sensed imagery / Shidong Wang in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)PermalinkAdversarial defenses for object detectors based on Gabor convolutional layers / Abdollah Amirkhani in The Visual Computer, vol 38 n° 6 (June 2022)PermalinkAssessing and mapping landslide susceptibility using different machine learning methods / Osman Orhan in Geocarto international, vol 37 n° 10 ([01/06/2022])PermalinkBeyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification / Yongqiang Mao in ISPRS Journal of photogrammetry and remote sensing, vol 188 (June 2022)PermalinkConstraint-based evaluation of map images generalized by deep learning / Azelle Courtial in Journal of Geovisualization and Spatial Analysis, vol 6 n° 1 (June 2022)PermalinkContext-aware network for semantic segmentation toward large-scale point clouds in urban environments / Chun Liu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 6 (June 2022)PermalinkCoupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction / Tianhong Zhao in Computers, Environment and Urban Systems, vol 94 (June 2022)PermalinkDetecting interchanges in road networks using a graph convolutional network approach / Min Yang in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)PermalinkDiffusionNet: discretization agnostic learning on surfaces / Nicholas Sharp in ACM Transactions on Graphics, TOG, Vol 41 n° 3 (June 2022)PermalinkExtracting the urban landscape features of the historic district from street view images based on deep learning: A case study in the Beijing Core area / Siming Yin in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)PermalinkFeature-selection high-resolution network with hypersphere embedding for semantic segmentation of VHR remote sensing images / Hanwen Xu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 6 (June 2022)PermalinkHyperNet: A deep network for hyperspectral, multispectral, and panchromatic image fusion / Kun Li in ISPRS Journal of photogrammetry and remote sensing, vol 188 (June 2022)PermalinkLarge-scale automatic identification of urban vacant land using semantic segmentation of high-resolution remote sensing images / Lingdong Mao in Landscape and Urban Planning, vol 222 (June 2022)PermalinkLine-based deep learning method for tree branch detection from digital images / Rodrigo L. S. Silva in International journal of applied Earth observation and geoinformation, vol 110 (June 2022)PermalinkPrecise crop classification of hyperspectral images using multi-branch feature fusion and dilation-based MLP / Haibin Wu in Remote sensing, vol 14 n° 11 (June-1 2022)Permalink